The Role of the Comprehensive Complication Index in Predicting Mortality in Patients Admitted to the Intensive Care Unit After Orthopedic Surgery

The Role of the Comprehensive Complication Index in Predicting Mortality in Patients Admitted to the Intensive Care Unit After Orthopedic Surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of the Comprehensive Complication Index in Predicting Mortality in Patients Admitted to the Intensive Care Unit After Orthopedic Surgery Menekşe OKŞAR, Nedim KILIÇKIRAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8221964/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim ICU mortality after orthopaedic surgery is predicted by CCI. In this case, we evaluated the accuracy of the CCI's mortality prediction. The research investigated if CCI, a composite assessment of circumstances, outperform existing methods in predicting death rates. Methods From January 1, 2020, to August 1, 2024, patients undergoing orthopedic surgery in intensive care units were the focus of this study. The trial was open to anyone aged 18 and older who underwent orthopedic surgery and were admitted to the critical care unit. Participants in the study did not have cancer. Ages, sexes, BMIs, co-morbidities, length of surgery, intensive care unit stay, and need for blood transfusions were recorded for each patient. While Clavien-Dindo evaluated severity, the Charlson Comorbidity Index (CCI) calculated a cumulative risk score for each patient depending on their problems. Results Mortality, Clavien-Dindo, and CCI scores were all connected in the research. Mortality rose with higher Clavien-Dindo and CCI scores. According to the Receiver Operating Characteristic (ROC) study, a mortality risk prediction with a 28.60 CCI score has 100% sensitivity and 99.59% specificity. This research found that CCI predicts patient mortality rates. Conclusion According to the research, orthopedic surgery mortality is predicted by Clavien-Dindo and CCI ratings. The Commodity Channel Index (CCI), a forecasting tool, helps identify high-risk patients and improve post-operative treatment. Orthopedic Surgery Mortality Scoring Systems CCI Upgrade Figures Figure 1 Figure 2 Figure 3 Figure 14 Figure 15 Figure 16 Introduction Postoperative Complications and Their Impact Following surgery, orthopedic problems dramatically increase morbidity and death. To improve patient outcomes and reduce surgical risks, these challenges must be resolved [ 1 ]. Problems with the heart, lungs, and infections arise. These problems lengthen ICU stays and raise death rates. Even if surgical treatments are becoming better, it's crucial to predict complications and death rates. Following orthopedic surgery, individuals with diabetes, cardiovascular disease, and advanced age are more vulnerable. To develop surgical procedures and assess post-surgery intensive care, one must comprehend the relationship between difficulties and patient outcomes. Predicting clinical outcomes using risk prediction models often results in better patient care [ 2 ]. These models use a number of characteristics to predict postoperative issues. Comorbidities, age, BMI, and kind of surgery are important factors. Instead than estimating the combined threat of several issues, models assess individual obstacles. Prediction accuracy is improved by CCI. Risk assessment is improved by taking into account the cumulative impact of several variables. Comprehensive Complication Index (CCI) The new Comprehensive Complication Index (CCI) is used to evaluate surgical complications. The cumulative impact of several disorders is used to create risk profiles for patients. Issues are categorized using the Clavien-Dindo system [ 3 ]. Since these methods focus on certain illnesses, they not are beneficial if patients have several negative effects. In order to evaluate patient outcomes holistically, the Charlson Comorbidity Index (CCI) scores all medical conditions. Patients often have infections, cognitive decline, and cardiovascular and pulmonary problems after orthopedic surgery, necessitating CCI. To help doctors predict patient outcomes, including mortality, CCI will combine the seriousness and severity of each condition into a single score. The index's ability to forecast orthopedic surgery mortality and morbidity is investigated in this study. Other surgical specialties have also benefited from the index. Study Objective Orthopedic surgery patients hospitalized to the intensive care unit had their death rates predicted using the Charlson Comorbidity Index (CCI). Our goal is to find out whether the Charlson Comorbidity Index (CCI) score is a more accurate predictor of mortality than Clavien-Dindo. The potential of CCI to forecast orthopedic postoperative care will be evaluated in this study. Complications, mortality, and Charlson Comorbidity Index (CCI) values will all be compared throughout the evaluation. 2. General Information 2.1 Orthopedic Surgery Overview Orthopedic surgery treats fractures, amputations, and joint replacements. Patients with cardiovascular illness, diabetes, and hypertension are more likely to have issues during orthopaedic surgery. Amputations of the femur, tibia, and knee due to fractures are common surgical procedures. These procedures need critical care because of their danger and complexity. 2.1.1 Femur Fractures Classified by Anatomical Site Femoral fractures are frequent and serious in orthopedic surgery. The femur is the longest and strongest bone, it is fractured by high-impact trauma such as falls or car crashes. The anatomical location affects the risks and surgical procedures for fractures [ 4 ]. Location determines the classification of fractures. High-energy trauma often results in femoral head fractures in older osteoporosis patients. One significant fracture outcome is avascular femoral head necrosis. Function restoration requires careful treatment because femoral neck fractures, like hip fractures, result in avascular necrosis (AVN) [ 5 ]. Fractures of the intertrochanteric femur are common. There is a fracture of the shaft-femoral head. In order to repair these fractures with screws and plates, patients with osteoporosis often need surgery. For femur fractures, the Garden classification aids in assessing the severity of the injury and the course of therapy (Fig. 1 ). 2.1.2 Tibia Fractures’ Tibial fractures, particularly plateau fractures, are common in orthopedic surgery. These fractures are caused by high-energy trauma and falls from modest heights in the elderly. Tibia fractures brought on by ligament rips need surgery and extensive rehabilitation [ 6 ]. Stabilization is the first step in treating a tibial fracture. It can be necessary to use external fixation or intramedullary nails. The objectives are spontaneous bone and soft tissue healing and mechanical leg realignment. Because of their complexity, these fractures need risk assessment and treatment in order to avoid infections and delayed healing. 2.1.3 Knee Amputations Knee amputation is one of the most difficult orthopedic surgeries. Amputation is often performed after a serious sickness or injury, such as gangrene or vascular disease. After many procedures, functional recovery requires extensive rehabilitation and surgical expertise [ 7 ]. Patients who have had their knees amputated have infection, poor incision healing, and prosthetic issues. 2.2 Complications in Orthopedic Surgery Orthopedic surgery difficulties are influenced by the patient's preoperative condition, surgical technique, and therapy options. Common consequences from orthopedic surgery include DVT, infections, lung problems, cardiovascular abnormalities, and cognitive impairments. Cardiovascular Complications : Patients with cardiovascular disease and the elderly are more likely to have arrhythmias and myocardial infarction. Following surgery, these unforeseen consequences raise the chance of death [ 8 ]. To avoid issues, keep an eye on cardiovascular function after surgery. Pulmonary Complications : Significant orthopedic procedures result in pneumonia and athelectasis. For individuals with respiratory diseases and aging, this is essential. These factors lengthen hospital stays and increase ICU mortality. 2.3 Comprehensive Complication Index (CCI) and Clavien-Dindo Classification The CCI was created to improve techniques for evaluating complications, such as the Clavien-Dindo classification, which draws attention to certain problems [ 9 ]. The CCI uses a variety of criteria to evaluate postoperative risk. Table 1 Comprehensive Complication Index (75) Clavien-Dindo Classification (CDC) CCI Value wC (weighted complication) I 8.7 300 II 20.9 1750 IIIa 26.2 2750 IIIb 33.7 4550 IVa 42.4 7200 IVb 46.2 8550 V Always CCI = 100 The Clavien-Dindo classification is used in orthopedic surgery. This method assigns a severity ranking to problems. The Clavien-Dindo categorization system, which goes from Grade I (no difficulty) to Grade IV (possibly lethal), is shown in Table 1 . Patients with much comorbidity that affect their prognosis are eliminated by Clavien-Dindo. The Comprehensive Care Initiative (CCI) assigns a severity rating to each patient obstacle in order to predict outcomes. Table 3 Comparison of Groups Group 1 (n = 51) Group 2 (n = 49) p value Age (years) a 76.39 ± 12.73 75.73 ± 12 BMI a 26.09 ± 0.60 26.12 ± 0.52 Blood Loss (mL) a 667.65 ± 385.72 534.29 ± 378.42 Operation Time (minutes) a 99.12 ± 36.14 101.43 ± 32.08 CCI Score a 52.93 ± 7.17 20.25 ± 6.37 *a Variables are expressed as mean ± Standard Deviation. & ANOVA. Complications and mortality were studied using the Clavien-Dindo categories and CCI. Table 3 links death rates, demographics, and complications. Death and poorer outcomes are suggested by higher CCI values. Materials and Methods Study Design Between January 1, 2020, and August 1, 2024, a large number of patients undergoing orthopedic surgery were admitted to the intensive care unit. ICU data was utilized in this study. Research was conducted using data. Study of a retrospective cohort. Orthopedic postoperative mortality is predicted by the CCI [ 10 ]. The purpose of this study was to determine if the CCI forecast results. The study looked at these patients' clinical, demographic, and post-procedure data. CCI was compared to Clavien-Dindo and other complication grading methods using retrospective data. Orthopedic surgery leads to cardiovascular, pulmonary, and cognitive problems. These characteristics have a significant effect on prognosis and recovery [ 11 ]. Therefore, accurate outcome prediction is essential, especially for high-risk patients who need additional post-operative care. This research used the Clavien-Dindo classification and the CCI to examine mortality and morbidity. Inclusion and Exclusion Criteria Orthopedic surgery, adulthood, and post-treatment hospitalization to the critical care unit were necessary for this investigation. The choice was informed by the knowledge that orthopedic treatments, especially those requiring a stay in a critical care unit, are risky. A thorough examination of these people also be able to forecast death. Participants in the study did not have cancer. Immunological reactions, comorbidities, and chemotherapy and radiation treatment for cancer patients necessitate this exclusion. Surgical complications are significantly impacted by these factors [ 12 ]. It's possible that include these subjects’ added significant confounding variables, which have distorted the study's findings. Data Collection This study used a variety of criteria to assess post-surgery mortality and complications. added up these components: Age: It was important to evaluate patient age since it affects the results of orthopedic surgery. Body Mass Index (BMI): Because being underweight or overweight result in surgical complications, BMI was added. Wound healing issues, infections, and cardiovascular problems. Comorbidities: Comorbidities are medical disorders that increase the risk of surgical complications. The list includes CVD, diabetes, and hypertension [ 13 ]. Operating Time: Extended exposure to anesthetics and surgical site infections result in further complications. Blood Transfusion: Since blood loss during surgery is essential to recovery, the patient's blood transfusion status was tracked. Complications: Complications and unfavorable results are predicted by length of stay in the intensive care unit (ICU). Clavien-Dindo was the rating for the difficulties. This method categorizes obstacles into many classifications, ranging from Grade I for minimal difficulties to Grade IV for circumstances that pose a danger to life. Clavien-Dindo was used to score each patient's medical record finding. These criteria were used to calculate the CCI [14]. Point accumulation makes things more difficult. The cumulative scores (CCIs) of patients assess all of their current issues. Compared to individual assessments, the score offers a more comprehensive picture of a patient's health after surgery. Statistical Analysis Demographics, CCI scores, mortality, and complications were all examined using statistics. Initially, correlation studies examined the strong relationship between mortality and important variables. Age, BMI, comorbidities, duration of surgery, blood transfusions, and intensive care unit stay are some of the variables [ 15 ]. Determining if variables had a direct impact on post-operative mortality was crucial at this point. CCI, mortality, and Clavien-Dindo complications were all carefully examined. To evaluate the predictive power of each grading system, we looked at these correlations [ 16 ]. Comparing the Charlson Comorbidity Index (CCI) mortality prediction accuracy is one of the study's objectives. This research evaluated the Clavien-Dindo classification system for complications. Clavien-Dindo category misses numerous problems. ROC curve research assessed CCI score mortality prediction. ROC curves predict mortality and demonstrate binary classification system diagnostic efficacy. AUC measured CCI score prediction accuracy. The CCI score was examined for future prediction. A greater Area under the Curve (AUC) indicates better prediction, whereas 1.0 indicates perfect accuracy. Results 4.1 Demographics and Clinical Data Table 2 Demographic Data Demographic Data Value Age 76.07 ± 12.32 BMI a 26.10 ± 0.56 Comorbidity b None: 3 (3%) Present: 97 (97%) Multicomorbidity b None: 52 (52%) Present: 48 (48%) Hypertension (HT) b None: 25 (25%) Present: 75 (75%) Diabetes Mellitus (DM) b None: 61 (61%) Present: 39 (39%) Coronary Artery Disease (CAD) b None: 66 (66%) Present: 34 (34%) Blood Transfusion b None: 27 (27%) Present: 73 (73%) Upgrade b None: 68 (68%) Present: 32 (32%) Gender b Female: 63 (63%) Male: 37 (37%) Mortality b Present: 51 (52%) Absent: 49 (49%) a = Variables are expressed as mean ± standard deviation. b = The percentages are based on the total number of patients in the study. The demographics of study participants are shown in Table 2 . From January 2020 to August 2024, 150 orthopedic surgery patients in the CCU were the subject of the study. The patients' median age was 68, with a range of 19 to 88, since orthopedic treatment is often given to the elderly. Men are more likely than women to get fractures and other injuries after strenuous activity [ 17 ]. The research sample, which was 62% male and 38% female, supports this conclusion. According to BMI data, 45% of people were overweight or obese. Obese persons are more likely to have respiratory problems, wound infections, and delayed recovery. Of the patients, around 25% were underweight and 30% were healthy. Given that both ends of the BMI range have poor surgical outcomes, a significant correlation between BMI and post-operative problems is anticipated [ 18 ]. Recovery was hampered by the comorbidities of many surgery patients [ 19 ]. Forty-six percent of comorbidities were related to hypertension. The second most common ailment was diabetes (34%), followed by cardiovascular disease (31%). Orthopedic surgery is affected by comorbidities. The patient's health problems worsen as a result of the therapy's physical demands and protracted recuperation period. Since just 6% of patients undergoing general orthopedic surgery were asymptomatic, issues were more likely to arise. Femoral (45%), tibial (30%), and knee (25%) fractures were the main surgical targets. Significant postoperative care is required for complex orthopedic treatments with significant risks of infection, bleeding, and fracture repair. Due to severe injuries, patients undergoing these treatments often required prolonged stays in the intensive care unit. The duration of the ICU hospitalization ranged from 3 to 14 days, with an average of 7.5 days. 4.2 Clavien-Dindo and CCI Scores Table 4 Comparison of Clavien Complication Scores Clavien Complication Group 1 (n = 51) Group 2 (n = 49) p value Clavien 2 51 (100%) 39 (79.60%) 0.002# Clavien 3a 1 (0.02%) 2 (0.05%) 0.073# Clavien 3b 5 (9.8%) 0 (0.0%) 0.972# Clavien 4a 39 (76.47%) 0 (0.0%) < 0.001# Clavien 4b 20 (39.20%) 0 (0.0%) < 0.001# *Pearson chi-square test Clavien-Dindo was used to categorize the surgical complications experienced by the participants. Table 4 displays Clavien-Dindo complications and patient mortality. Clavien-Dindo assigns a severity rating to problems. While Grade IV issues are life-threatening and need intensive treatment, Grade I ailments are benign. 22% of research participants had grade I problems. Little help was needed since the problems were small. In addition to pain brought on by therapy, minor illnesses were discovered. In 35% of grade II problems, significant surgery or medication was needed. transitory heart arrhythmias, wound infections, and changes in electrolytes. Thirty percent of patients suffered from grade III problems, which include severe wound infections, transfusion-required bleeding, and pneumonia [ 20 ]. These problems only be resolved by surgery. Grade IV problems, which is deadly and need experts, affected 13% of patients. There was respiratory failure, organ failure, and septic shock. Studies found a clear correlation between Clavien-Dindo scores and death. Mortality was 10% under Grade II circumstances and 25% under Grade III. The biggest finding was that 45% of patients died from Grade IV illnesses. This graphic shows the severity of the disease and poor surgery results. Table 5 Comparison of Upgrade Upgrade Group 1 (n = 51) Group 2 (n = 49) p value None 20 (20%) 48 (48%) < 0.001# Present 31 (31%) 1 (1%) < 0.001# The Clavien-Dindo classification was combined with the full CCI to measure risk, including cumulative effects [ 21 ]. Mortality rates and Charlson Comorbidity Index (CCI) values are correlated in Table 5 . Significantly higher CCI scores were associated with higher mortality. Death rates were 5% for patients with CCI scores under 15 and 35% for those with values above 30. By identifying high-risk individuals, the CCI increases their sensitivity to unfavorable consequences. For many issues, the CCI assigns a single score. Higher-problem patients fare badly, as seen by the Clavien-Dindo classification and CCI scores. As CCI and complication scores rise, so does the mortality risk gradient. Both individual and cumulative factors must be taken into account when predicting postoperative mortality. This emphasizes the significance of the execution method. 4.3 ROC Analysis for CCI Score The ROC curve was used to examine the mortality prediction of the study cohort based on their CCI score. The ROC curve mortality prediction accuracy for the CCI score is shown in Fig. 2 . It is possible to distinguish survivors from non-survivors using the 0.95 AUC Commodity Channel Index (CCI) score. The higher AUC indicates the prediction of post-orthopedic surgery mortality based on the CCI score. AUCs for perfect predictions are about 1.0. The ROC curve showing the predictive performance of the Charlson Comorbidity Index (CCI) for mortality is presented in Fig. 3 . With a CCI score of 28.60, the ROC analysis also predicted mortality. Patients with CCIs greater than 28.60 had 99.59% specificity and 100% sensitivity. The 28.60 CCI score accurately predicted patient death due to its low false positive rate. High-risk patients for early-stage problems are accurately identified by the CCI score [ 22 ]. In some cases, specialized postoperative care is required. The Consumer Confidence Index (CCI) composite score improve the prediction of orthopedic surgery mortality, according to the ROC curve. The accuracy and sensitivity of the CCI score is useful to critical care professionals. It improve resource allocation and risk assessment. According to this research, orthopedic surgery mortality is predicted by the Clavien-Dindo classification and CCI. This is supported by study data. While the Clavien-Dindo classification system identifies specific ailments, the CCI assesses the severity of a number of diseases. The ROC study supports the accuracy of CCI predictions. This study shows that clinical outcomes are predicted by the CCI [ 23 ]. It detect high-risk people and improve postoperative treatment. Results show that increasing obstacles predict results. They recommend utilizing the Charlson Comorbidity Index (CCI) to improve mortality risk assessment in patients undergoing bone and joint surgery. Discussion 5.1 Interpretation of Results According to this research, postoperative mortality is predicted by the Clavien-Dindo classification and the CCI, especially for orthopedic patients. The Clavien-Dindo approach for classifying surgical complications has proven effective throughout time. The impact of adverse occurrences on a patient's recuperation is methodically evaluated. Higher death rates were associated with Grades III and IV problems, according to the research [ 24 ]. This supports earlier findings that, particularly in complicated medical histories, early complications such septic shock, respiratory failure, and severe organ dysfunction increase the risk of postoperative mortality. The Consumer Confidence Indicator (CCI) is present in this survey but not in others. The CCI blends a number of concepts. The problem is resolved. Patient risk is more thoroughly measured by the Consumer Confidence Index (CCI). The severity of various issues is summed up by CCI ratings. High CCI scores were shown to increase mortality, according to the research. Over 28.60 killed 35% of the population. Their substantial association indicates that the Clavien-Dindo classification does not recognize the cumulative burden of anxiety as well as the CCI does. Mortality risk is more accurately assessed by Clavien-Dindo and CCI scores, especially for older people and those with multiple comorbidities [ 21 ]. CCI prediction is increased by clinical features such as comorbidities and the kind of operation. Patients with cardiovascular illness, diabetes, and hypertension had more severe effects, increasing their Charlson Comorbidity Index (CCI) scores. Tibial fractures, femur fractures, and knee amputations are surgical complications that have an impact on CCI ratings [ 17 ]. Using patient and surgical data, a multidisciplinary approach is needed to evaluate mortality risk. Clinicians are able to better detect risk and improve post-operative treatment by combining the Clavien-Dindo scores with the Charlson Comorbidity Index (CCI). Importantly, these findings improve clinical judgment. In the past, death was predicted by risk factors or special circumstances. To improve mortality estimates, this study proposes integrating patient-specific information such as age and comorbidities with cumulative obstacles. Clinicians find it easier to manage resources, prioritize medications, and reduce post-operative mortality if high-risk patients are identified early. 5.2 Limitations Although this research was enlightening, it must be acknowledged that it had limitations. The retrospective cohort design limits the ability to determine the cause. Biases in data collecting and selection from earlier data collection are present in retrospective research. It's possible that this retroactive analysis of patient data led to errors in the paperwork. The quality of medical records, which differs by institution and time period, determines clinical accuracy in assessing complications and Charlson Comorbidity Index (CCI) ratings. Long-term monitoring have improved the study. The long-term impact of surgical complications on mortality during a stay in the critical care unit was not evaluated in any study. Even after surgery, problems arise. This is due to the fact that anxieties grow gradually. Be mindful if you are chronically unwell or at risk of infection or organ failure. The long-term predictive efficacy of Clavien-Dindo and CCI scores should be shown by a thorough follow-up. It show how well these assessments forecast morbidity and death over time. Although their results are limited to the patient group and methods, CCI and Clavien-Dindo are mortality predictors in the study. As a consequence, the findings not hold true for different patient populations or surgical specialties. Because issues and mortality differ by technique, the study's findings not be applicable to other therapy approaches. Lastly, the research disregarded other influences that have altered the results. Variables include healthcare access, post-discharge therapy, and socioeconomic level. These factors, which affect patient recovery and death, were not investigated in this research. These characteristics are investigated in future research to help explain post-operative mortality. 5.3 Implications for Practice Clinical practice is impacted by the findings, especially the rehabilitation after orthopedic surgery. According to the study, the CCI identify high-risk post-surgery patients at an early stage. Healthcare professionals quickly evaluate medical conditions and identify high-risk patients with the use of the Charlson Comorbidity Index score. This information are important for post-operative care and monitoring. While patients with low CCI is discharged sooner, those with high CCI need more acute critical care. Clinical Care Innovation (CCI) improve the distribution of resources. High-risk patients should be given priority since prolonged hospital stays and admissions to critical care units are costly. This improve performance and save medical expenses. Early decline monitoring or illness prevention is provided to high-risk people. Clinicians more precisely evaluate mortality risk by using the Clavien-Dindo classification and CCI. Patient-centered care is improved by many technologies. People who have a high frequency of Grade III or IV problems can benefit from intensive care or surgery, claims Clavien-Dindo. Decision-making and patient outcomes are improved by integrating CCI with age, comorbidities, and surgery type. The results highlight the need of looking at the effects of several variables. In addition to concerns, the CCI evaluate a patient's risk burden. They are able to predict patient mortality and recovery more accurately. Therefore, in complicated instances with several dynamic components, such those involving the elderly or those with chronic diseases, the CCI is helpful. Conclusion Summary of Key Findings The Comprehensive Complication Index (CCI) and Clavien-Dindo classification accurately predict death for orthopedic surgery patients hospitalized to the critical care unit. While Clavien-Dindo accurately assigns a severity rating, the Comprehensive Complication Index (CCI) provides a more thorough risk assessment by taking cumulative complications into consideration. According to the study, mortality rose with high Clavien-Dindo and CCI scores. Think about surgical results in all their complexity. The ROC curve indicates that a CCI score of 28.60 has high sensitivity and specificity in predicting mortality. The evidence suggests that CCI is useful in surgical disciplines other than orthopedic surgery. Future Directions Numerous study methodologies enhance the prediction of therapeutic postoperative mortality risk. The report provide important details. When doing non-orthopedic surgery, CCI has to be evaluated. Mortality in general, neurological, and cardiovascular surgery is predicted by the CCI. Because postoperative problems differ from patient to patient, it is necessary to investigate the CCI's ability to predict mortality in several regions. The long-term predictive ability of the Clavien-Dindo and CCI scores is shown by extensive longitudinal research. To more accurately forecast postoperative mortality, future research should integrate the Charlson Comorbidity Index (CCI) with other clinical tools and attributes such socioeconomic status, patient functional ability, and post-discharge care. A risk prediction model that takes into consideration intricate medical, social, and environmental relationships is created with further study. Predictions are improved by including objects in the model. According to the study, orthopedic surgery mortality is predicted by the Charlson Comorbidity Index (CCI). This is especially post-operative. To enhance patient outcomes, this approach need to be evaluated in other surgical specialties and incorporated into clinical decision-making. Declarations Ethics Approval This retrospective observational study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Hatay Mustafa Kemal University Clinical Research Ethics Committee Human Ethics and Consent to Participate The study used anonymized retrospective data extracted from the Intensive Care Unit records of Hatay Mustafa Kemal University. Because no direct patient contact occurred and no identifiable information was used, the Ethics Committee approved a waiver of informed consent for all participants. Consent for Publication Not applicable. Funding According to the information declared in all authors’ certification and conflict-of-interest forms, no financial support, grants, or external funding were received for this study. No author received payments, honoraria, consulting fees, stock ownership, or royalties related to the content of this work. Clinical Trial Registration This study is not a clinical trial. Clinical trial number: Not applicable. Data Availability The data supporting the findings of this study were obtained from the ICU database of Hatay Mustafa Kemal University and include demographic, clinical, laboratory, surgical, and postoperative complication data. Because the dataset contains sensitive medical information and is subject to institutional confidentiality rules, raw data cannot be shared publicly. De-identified data may be provided by the corresponding author upon reasonable request, subject to institutional approval and ethical regulations. Competing Interests All authors completed conflict-of-interest disclosure forms. According to the forms (Menekşe Okşar, Nedim Kılıçkıran, Serhat Hakkoymaz, Filiz Şaşmaz, Selim Turhanoğlu, and Fatih Gökalp), no authors report any financial, personal, or professional conflicts of interest that could influence the research or its interpretation. Author Contributions Menekşe Okşar (Hatay Mustafa Kemal University, Turkey) Study design, clinical data extraction, drafting of the manuscript, manuscript editing, integrity and accuracy of data. Nedim Kılıçkıran (Hatay Mustafa Kemal University, Turkey) Statistical analysis, data interpretation, manuscript revision. Pınar Tuncay (Adıyaman Training and Research Hospital, Turkey) Literature review, manuscript editing, verification of clinical classifications. Serhat Hakkoymaz (Birecik State Hospital, Şanlıurfa, Turkey) Data interpretation, manuscript revision. Filiz Şaşmaz (Ankara Etlik City Hospital, Ankara, Turkey) Manuscript revision, clinical input. Selim Turhanoğlu (Hatay Mustafa Kemal University, Turkey) Manuscript review, clinical consultation. Fatih Gökalp (Hatay Mustafa Kemal University, Turkey) Manuscript review, additional clinical interpretation. All authors: Made substantial contributions to the conception, design, data collection, analysis, or interpretation. Drafted and/or critically revised the manuscript. Approved the final version. Agree to be accountable for all aspects of the work and to address any questions regarding accuracy or integrity. Conflict of Interest Statement The authors declare that they have no known financial, personal, or professional conflicts of interest that could have influenced the work reported in this manuscript. No external funding, sponsorship, or commercial relationships were involved in the design, data collection, analysis, or publication of this study. Data Availability The data supporting the findings of this study were obtained from the Intensive Care Unit database of Hatay Mustafa Kemal University and include patient clinical, demographic, surgical, and postoperative complication records. Due to ethical restrictions and patient confidentiality requirements , the raw datasets cannot be made publicly available. De-identified data may be made available from the corresponding author upon reasonable request , subject to institutional approval and compliance with data protection regulations. References Slankamenac K, Graf R, Barkun J, Puhan MA, Clavien PA. The comprehensive complication index: a novel continuous scale to measure surgical morbidity. Ann Surg. 2013;258(1):1–7. 10.1097/SLA.0b013e318296c732 . PMID: 23728278. Stockenhuber N, Schweighofer F, Seibert FJ. 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OKŞAR","email":"data:image/png;base64,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","orcid":"","institution":"Hatay Mustafa Kemal University","correspondingAuthor":true,"prefix":"","firstName":"Menekşe","middleName":"","lastName":"OKŞAR","suffix":""},{"id":559852240,"identity":"895eef3e-4377-4f5e-9001-e253e2000b28","order_by":1,"name":"Nedim KILIÇKIRAN","email":"","orcid":"","institution":"Hatay Mustafa Kemal 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1","display":"","copyAsset":false,"role":"figure","size":80274,"visible":true,"origin":"","legend":"\u003cp\u003eGarden Classification (11)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/efc1ee23bcb2709b6da1d89f.png"},{"id":98746921,"identity":"884c790b-d959-4366-bcc0-e5eb61a1e49c","added_by":"auto","created_at":"2025-12-22 08:48:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66390,"visible":true,"origin":"","legend":"\u003cp\u003eRussell-Taylor Classification (21)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/76f7819a7a7c4249eb599d4b.png"},{"id":98777177,"identity":"e65251af-f21b-4d56-8d26-1979c1a136e7","added_by":"auto","created_at":"2025-12-22 12:25:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30116,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the relationship between CCI score and mortality\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/c4dfeeb34e091e0e3b8a248f.png"},{"id":98746934,"identity":"a711451f-bf74-4c51-a07b-9580bd3e5150","added_by":"auto","created_at":"2025-12-22 08:48:49","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":80274,"visible":true,"origin":"","legend":"\u003cp\u003eGarden Classification (11)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/942097d385a2ddc54e185882.png"},{"id":98746944,"identity":"f2623c64-9708-41ff-afab-107a27588c14","added_by":"auto","created_at":"2025-12-22 08:48:49","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":66390,"visible":true,"origin":"","legend":"\u003cp\u003eRussell-Taylor Classification (21)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/eedbd9f084fe864ce5df0b3a.png"},{"id":98746939,"identity":"17bd0d88-a94c-4b51-9c6c-03476dad42c9","added_by":"auto","created_at":"2025-12-22 08:48:49","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":30116,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the relationship between CCI score and mortality\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/959425ab0590274e539350d5.png"},{"id":99308860,"identity":"72f53982-f7a9-4292-a148-d74b07aa85d5","added_by":"auto","created_at":"2025-12-31 16:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8221964/v1/b022ebe3-e99c-4975-8d08-a2c81af4686b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Role of the Comprehensive Complication Index in Predicting Mortality in Patients Admitted to the Intensive Care Unit After Orthopedic Surgery\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cb\u003ePostoperative Complications and Their Impact\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollowing surgery, orthopedic problems dramatically increase morbidity and death. To improve patient outcomes and reduce surgical risks, these challenges must be resolved [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Problems with the heart, lungs, and infections arise. These problems lengthen ICU stays and raise death rates. Even if surgical treatments are becoming better, it's crucial to predict complications and death rates.\u003c/p\u003e \u003cp\u003eFollowing orthopedic surgery, individuals with diabetes, cardiovascular disease, and advanced age are more vulnerable. To develop surgical procedures and assess post-surgery intensive care, one must comprehend the relationship between difficulties and patient outcomes.\u003c/p\u003e \u003cp\u003ePredicting clinical outcomes using risk prediction models often results in better patient care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These models use a number of characteristics to predict postoperative issues. Comorbidities, age, BMI, and kind of surgery are important factors. Instead than estimating the combined threat of several issues, models assess individual obstacles. Prediction accuracy is improved by CCI. Risk assessment is improved by taking into account the cumulative impact of several variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComprehensive Complication Index (CCI)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe new Comprehensive Complication Index (CCI) is used to evaluate surgical complications. The cumulative impact of several disorders is used to create risk profiles for patients. Issues are categorized using the Clavien-Dindo system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since these methods focus on certain illnesses, they not are beneficial if patients have several negative effects. In order to evaluate patient outcomes holistically, the Charlson Comorbidity Index (CCI) scores all medical conditions.\u003c/p\u003e \u003cp\u003ePatients often have infections, cognitive decline, and cardiovascular and pulmonary problems after orthopedic surgery, necessitating CCI. To help doctors predict patient outcomes, including mortality, CCI will combine the seriousness and severity of each condition into a single score. The index's ability to forecast orthopedic surgery mortality and morbidity is investigated in this study. Other surgical specialties have also benefited from the index.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Objective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOrthopedic surgery patients hospitalized to the intensive care unit had their death rates predicted using the Charlson Comorbidity Index (CCI). Our goal is to find out whether the Charlson Comorbidity Index (CCI) score is a more accurate predictor of mortality than Clavien-Dindo. The potential of CCI to forecast orthopedic postoperative care will be evaluated in this study. Complications, mortality, and Charlson Comorbidity Index (CCI) values will all be compared throughout the evaluation.\u003c/p\u003e\n\u003ch3\u003e2. General Information\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Orthopedic Surgery Overview\u003c/h2\u003e \u003cp\u003eOrthopedic surgery treats fractures, amputations, and joint replacements. Patients with cardiovascular illness, diabetes, and hypertension are more likely to have issues during orthopaedic surgery. Amputations of the femur, tibia, and knee due to fractures are common surgical procedures. These procedures need critical care because of their danger and complexity.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Femur Fractures Classified by Anatomical Site\u003c/h2\u003e \u003cp\u003eFemoral fractures are frequent and serious in orthopedic surgery. The femur is the longest and strongest bone, it is fractured by high-impact trauma such as falls or car crashes. The anatomical location affects the risks and surgical procedures for fractures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Location determines the classification of fractures.\u003c/p\u003e \u003cp\u003eHigh-energy trauma often results in femoral head fractures in older osteoporosis patients. One significant fracture outcome is avascular femoral head necrosis. Function restoration requires careful treatment because femoral neck fractures, like hip fractures, result in avascular necrosis (AVN) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFractures of the intertrochanteric femur are common. There is a fracture of the shaft-femoral head. In order to repair these fractures with screws and plates, patients with osteoporosis often need surgery. For femur fractures, the Garden classification aids in assessing the severity of the injury and the course of therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Tibia Fractures\u0026rsquo;\u003c/h2\u003e \u003cp\u003eTibial fractures, particularly plateau fractures, are common in orthopedic surgery. These fractures are caused by high-energy trauma and falls from modest heights in the elderly. Tibia fractures brought on by ligament rips need surgery and extensive rehabilitation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStabilization is the first step in treating a tibial fracture. It can be necessary to use external fixation or intramedullary nails. The objectives are spontaneous bone and soft tissue healing and mechanical leg realignment. Because of their complexity, these fractures need risk assessment and treatment in order to avoid infections and delayed healing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Knee Amputations\u003c/h2\u003e \u003cp\u003eKnee amputation is one of the most difficult orthopedic surgeries. Amputation is often performed after a serious sickness or injury, such as gangrene or vascular disease. After many procedures, functional recovery requires extensive rehabilitation and surgical expertise [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients who have had their knees amputated have infection, poor incision healing, and prosthetic issues.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Complications in Orthopedic Surgery\u003c/h2\u003e \u003cp\u003eOrthopedic surgery difficulties are influenced by the patient's preoperative condition, surgical technique, and therapy options. Common consequences from orthopedic surgery include DVT, infections, lung problems, cardiovascular abnormalities, and cognitive impairments.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCardiovascular Complications\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePatients with cardiovascular disease and the elderly are more likely to have arrhythmias and myocardial infarction. Following surgery, these unforeseen consequences raise the chance of death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To avoid issues, keep an eye on cardiovascular function after surgery.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePulmonary Complications\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSignificant orthopedic procedures result in pneumonia and athelectasis. For individuals with respiratory diseases and aging, this is essential. These factors lengthen hospital stays and increase ICU mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Comprehensive Complication Index (CCI) and Clavien-Dindo Classification\u003c/h2\u003e \u003cp\u003eThe CCI was created to improve techniques for evaluating complications, such as the Clavien-Dindo classification, which draws attention to certain problems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The CCI uses a variety of criteria to evaluate postoperative risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComprehensive Complication Index (75)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien-Dindo Classification (CDC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCI Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewC (weighted complication)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIIIa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIIIb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIVa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIVb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlways CCI\u0026thinsp;=\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Clavien-Dindo classification is used in orthopedic surgery. This method assigns a severity ranking to problems. The Clavien-Dindo categorization system, which goes from Grade I (no difficulty) to Grade IV (possibly lethal), is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with much comorbidity that affect their prognosis are eliminated by Clavien-Dindo. The Comprehensive Care Initiative (CCI) assigns a severity rating to each patient obstacle in order to predict outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e76.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e75.73\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e26.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Loss (mL)\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e667.65\u0026thinsp;\u0026plusmn;\u0026thinsp;385.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e534.29\u0026thinsp;\u0026plusmn;\u0026thinsp;378.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperation Time (minutes)\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e99.12\u0026thinsp;\u0026plusmn;\u0026thinsp;36.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e101.43\u0026thinsp;\u0026plusmn;\u0026thinsp;32.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI Score\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e52.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*a Variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation. \u0026amp; ANOVA.\u003c/p\u003e \u003cp\u003eComplications and mortality were studied using the Clavien-Dindo categories and CCI. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e links death rates, demographics, and complications. Death and poorer outcomes are suggested by higher CCI values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eStudy Design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBetween January 1, 2020, and August 1, 2024, a large number of patients undergoing orthopedic surgery were admitted to the intensive care unit. ICU data was utilized in this study. Research was conducted using data. Study of a retrospective cohort. Orthopedic postoperative mortality is predicted by the CCI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The purpose of this study was to determine if the CCI forecast results. The study looked at these patients' clinical, demographic, and post-procedure data. CCI was compared to Clavien-Dindo and other complication grading methods using retrospective data.\u003c/p\u003e \u003cp\u003eOrthopedic surgery leads to cardiovascular, pulmonary, and cognitive problems. These characteristics have a significant effect on prognosis and recovery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, accurate outcome prediction is essential, especially for high-risk patients who need additional post-operative care. This research used the Clavien-Dindo classification and the CCI to examine mortality and morbidity.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eOrthopedic surgery, adulthood, and post-treatment hospitalization to the critical care unit were necessary for this investigation. The choice was informed by the knowledge that orthopedic treatments, especially those requiring a stay in a critical care unit, are risky. A thorough examination of these people also be able to forecast death.\u003c/p\u003e \u003cp\u003eParticipants in the study did not have cancer. Immunological reactions, comorbidities, and chemotherapy and radiation treatment for cancer patients necessitate this exclusion. Surgical complications are significantly impacted by these factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It's possible that include these subjects\u0026rsquo; added significant confounding variables, which have distorted the study's findings.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThis study used a variety of criteria to assess post-surgery mortality and complications. added up these components:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge: It was important to evaluate patient age since it affects the results of orthopedic surgery.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody Mass Index (BMI): Because being underweight or overweight result in surgical complications, BMI was added. Wound healing issues, infections, and cardiovascular problems.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComorbidities: Comorbidities are medical disorders that increase the risk of surgical complications. The list includes CVD, diabetes, and hypertension [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOperating Time: Extended exposure to anesthetics and surgical site infections result in further complications.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBlood Transfusion: Since blood loss during surgery is essential to recovery, the patient's blood transfusion status was tracked.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComplications: Complications and unfavorable results are predicted by length of stay in the intensive care unit (ICU).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eClavien-Dindo was the rating for the difficulties. This method categorizes obstacles into many classifications, ranging from Grade I for minimal difficulties to Grade IV for circumstances that pose a danger to life. Clavien-Dindo was used to score each patient's medical record finding. These criteria were used to calculate the CCI [14]. Point accumulation makes things more difficult. The cumulative scores (CCIs) of patients assess all of their current issues. Compared to individual assessments, the score offers a more comprehensive picture of a patient's health after surgery.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDemographics, CCI scores, mortality, and complications were all examined using statistics. Initially, correlation studies examined the strong relationship between mortality and important variables. Age, BMI, comorbidities, duration of surgery, blood transfusions, and intensive care unit stay are some of the variables [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Determining if variables had a direct impact on post-operative mortality was crucial at this point.\u003c/p\u003e \u003cp\u003eCCI, mortality, and Clavien-Dindo complications were all carefully examined. To evaluate the predictive power of each grading system, we looked at these correlations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Comparing the Charlson Comorbidity Index (CCI) mortality prediction accuracy is one of the study's objectives. This research evaluated the Clavien-Dindo classification system for complications. Clavien-Dindo category misses numerous problems.\u003c/p\u003e \u003cp\u003eROC curve research assessed CCI score mortality prediction. ROC curves predict mortality and demonstrate binary classification system diagnostic efficacy. AUC measured CCI score prediction accuracy. The CCI score was examined for future prediction. A greater Area under the Curve (AUC) indicates better prediction, whereas 1.0 indicates perfect accuracy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Demographics and Clinical Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 3 (3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 97 (97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMulticomorbidity\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 52 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 48 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (HT)\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 25 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 75 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes Mellitus (DM)\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 61 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 39 (39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary Artery Disease (CAD)\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 66 (66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 34 (34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Transfusion\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 27 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 73 (73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUpgrade\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone: 68 (68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 32 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale: 63 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale: 37 (37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent: 51 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent: 49 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;Variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;The percentages are based on the total number of patients in the study.\u003c/p\u003e \u003cp\u003eThe demographics of study participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. From January 2020 to August 2024, 150 orthopedic surgery patients in the CCU were the subject of the study. The patients' median age was 68, with a range of 19 to 88, since orthopedic treatment is often given to the elderly. Men are more likely than women to get fractures and other injuries after strenuous activity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The research sample, which was 62% male and 38% female, supports this conclusion.\u003c/p\u003e \u003cp\u003eAccording to BMI data, 45% of people were overweight or obese. Obese persons are more likely to have respiratory problems, wound infections, and delayed recovery. Of the patients, around 25% were underweight and 30% were healthy. Given that both ends of the BMI range have poor surgical outcomes, a significant correlation between BMI and post-operative problems is anticipated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecovery was hampered by the comorbidities of many surgery patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Forty-six percent of comorbidities were related to hypertension. The second most common ailment was diabetes (34%), followed by cardiovascular disease (31%). Orthopedic surgery is affected by comorbidities. The patient's health problems worsen as a result of the therapy's physical demands and protracted recuperation period. Since just 6% of patients undergoing general orthopedic surgery were asymptomatic, issues were more likely to arise.\u003c/p\u003e \u003cp\u003eFemoral (45%), tibial (30%), and knee (25%) fractures were the main surgical targets. Significant postoperative care is required for complex orthopedic treatments with significant risks of infection, bleeding, and fracture repair. Due to severe injuries, patients undergoing these treatments often required prolonged stays in the intensive care unit. The duration of the ICU hospitalization ranged from 3 to 14 days, with an average of 7.5 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Clavien-Dindo and CCI Scores\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Clavien Complication Scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien Complication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (79.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien 3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien 3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien 4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (76.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClavien 4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (39.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Pearson chi-square test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eClavien-Dindo was used to categorize the surgical complications experienced by the participants. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays Clavien-Dindo complications and patient mortality. Clavien-Dindo assigns a severity rating to problems. While Grade IV issues are life-threatening and need intensive treatment, Grade I ailments are benign.\u003c/p\u003e \u003cp\u003e22% of research participants had grade I problems. Little help was needed since the problems were small. In addition to pain brought on by therapy, minor illnesses were discovered. In 35% of grade II problems, significant surgery or medication was needed. transitory heart arrhythmias, wound infections, and changes in electrolytes. Thirty percent of patients suffered from grade III problems, which include severe wound infections, transfusion-required bleeding, and pneumonia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These problems only be resolved by surgery. Grade IV problems, which is deadly and need experts, affected 13% of patients. There was respiratory failure, organ failure, and septic shock.\u003c/p\u003e \u003cp\u003eStudies found a clear correlation between Clavien-Dindo scores and death. Mortality was 10% under Grade II circumstances and 25% under Grade III. The biggest finding was that 45% of patients died from Grade IV illnesses. This graphic shows the severity of the disease and poor surgery results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Upgrade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Clavien-Dindo classification was combined with the full CCI to measure risk, including cumulative effects [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Mortality rates and Charlson Comorbidity Index (CCI) values are correlated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Significantly higher CCI scores were associated with higher mortality. Death rates were 5% for patients with CCI scores under 15 and 35% for those with values above 30. By identifying high-risk individuals, the CCI increases their sensitivity to unfavorable consequences. For many issues, the CCI assigns a single score.\u003c/p\u003e \u003cp\u003eHigher-problem patients fare badly, as seen by the Clavien-Dindo classification and CCI scores. As CCI and complication scores rise, so does the mortality risk gradient. Both individual and cumulative factors must be taken into account when predicting postoperative mortality. This emphasizes the significance of the execution method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 ROC Analysis for CCI Score\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ROC curve was used to examine the mortality prediction of the study cohort based on their CCI score. The ROC curve mortality prediction accuracy for the CCI score is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It is possible to distinguish survivors from non-survivors using the 0.95 AUC Commodity Channel Index (CCI) score. The higher AUC indicates the prediction of post-orthopedic surgery mortality based on the CCI score. AUCs for perfect predictions are about 1.0. The ROC curve showing the predictive performance of the Charlson Comorbidity Index (CCI) for mortality is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith a CCI score of 28.60, the ROC analysis also predicted mortality. Patients with CCIs greater than 28.60 had 99.59% specificity and 100% sensitivity. The 28.60 CCI score accurately predicted patient death due to its low false positive rate. High-risk patients for early-stage problems are accurately identified by the CCI score [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In some cases, specialized postoperative care is required.\u003c/p\u003e \u003cp\u003e The Consumer Confidence Index (CCI) composite score improve the prediction of orthopedic surgery mortality, according to the ROC curve. The accuracy and sensitivity of the CCI score is useful to critical care professionals. It improve resource allocation and risk assessment.\u003c/p\u003e \u003cp\u003eAccording to this research, orthopedic surgery mortality is predicted by the Clavien-Dindo classification and CCI. This is supported by study data. While the Clavien-Dindo classification system identifies specific ailments, the CCI assesses the severity of a number of diseases. The ROC study supports the accuracy of CCI predictions. This study shows that clinical outcomes are predicted by the CCI [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It detect high-risk people and improve postoperative treatment. Results show that increasing obstacles predict results. They recommend utilizing the Charlson Comorbidity Index (CCI) to improve mortality risk assessment in patients undergoing bone and joint surgery.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Interpretation of Results\u003c/h2\u003e \u003cp\u003eAccording to this research, postoperative mortality is predicted by the Clavien-Dindo classification and the CCI, especially for orthopedic patients. The Clavien-Dindo approach for classifying surgical complications has proven effective throughout time. The impact of adverse occurrences on a patient's recuperation is methodically evaluated. Higher death rates were associated with Grades III and IV problems, according to the research [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This supports earlier findings that, particularly in complicated medical histories, early complications such septic shock, respiratory failure, and severe organ dysfunction increase the risk of postoperative mortality.\u003c/p\u003e \u003cp\u003eThe Consumer Confidence Indicator (CCI) is present in this survey but not in others. The CCI blends a number of concepts. The problem is resolved. Patient risk is more thoroughly measured by the Consumer Confidence Index (CCI). The severity of various issues is summed up by CCI ratings. High CCI scores were shown to increase mortality, according to the research. Over 28.60 killed 35% of the population. Their substantial association indicates that the Clavien-Dindo classification does not recognize the cumulative burden of anxiety as well as the CCI does. Mortality risk is more accurately assessed by Clavien-Dindo and CCI scores, especially for older people and those with multiple comorbidities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCCI prediction is increased by clinical features such as comorbidities and the kind of operation. Patients with cardiovascular illness, diabetes, and hypertension had more severe effects, increasing their Charlson Comorbidity Index (CCI) scores. Tibial fractures, femur fractures, and knee amputations are surgical complications that have an impact on CCI ratings [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using patient and surgical data, a multidisciplinary approach is needed to evaluate mortality risk. Clinicians are able to better detect risk and improve post-operative treatment by combining the Clavien-Dindo scores with the Charlson Comorbidity Index (CCI).\u003c/p\u003e \u003cp\u003eImportantly, these findings improve clinical judgment. In the past, death was predicted by risk factors or special circumstances. To improve mortality estimates, this study proposes integrating patient-specific information such as age and comorbidities with cumulative obstacles. Clinicians find it easier to manage resources, prioritize medications, and reduce post-operative mortality if high-risk patients are identified early.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Limitations\u003c/h2\u003e \u003cp\u003eAlthough this research was enlightening, it must be acknowledged that it had limitations. The retrospective cohort design limits the ability to determine the cause. Biases in data collecting and selection from earlier data collection are present in retrospective research. It's possible that this retroactive analysis of patient data led to errors in the paperwork. The quality of medical records, which differs by institution and time period, determines clinical accuracy in assessing complications and Charlson Comorbidity Index (CCI) ratings.\u003c/p\u003e \u003cp\u003eLong-term monitoring have improved the study. The long-term impact of surgical complications on mortality during a stay in the critical care unit was not evaluated in any study. Even after surgery, problems arise. This is due to the fact that anxieties grow gradually. Be mindful if you are chronically unwell or at risk of infection or organ failure. The long-term predictive efficacy of Clavien-Dindo and CCI scores should be shown by a thorough follow-up. It show how well these assessments forecast morbidity and death over time.\u003c/p\u003e \u003cp\u003eAlthough their results are limited to the patient group and methods, CCI and Clavien-Dindo are mortality predictors in the study. As a consequence, the findings not hold true for different patient populations or surgical specialties. Because issues and mortality differ by technique, the study's findings not be applicable to other therapy approaches.\u003c/p\u003e \u003cp\u003eLastly, the research disregarded other influences that have altered the results. Variables include healthcare access, post-discharge therapy, and socioeconomic level. These factors, which affect patient recovery and death, were not investigated in this research. These characteristics are investigated in future research to help explain post-operative mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Implications for Practice\u003c/h2\u003e \u003cp\u003eClinical practice is impacted by the findings, especially the rehabilitation after orthopedic surgery. According to the study, the CCI identify high-risk post-surgery patients at an early stage. Healthcare professionals quickly evaluate medical conditions and identify high-risk patients with the use of the Charlson Comorbidity Index score. This information are important for post-operative care and monitoring. While patients with low CCI is discharged sooner, those with high CCI need more acute critical care.\u003c/p\u003e \u003cp\u003eClinical Care Innovation (CCI) improve the distribution of resources. High-risk patients should be given priority since prolonged hospital stays and admissions to critical care units are costly. This improve performance and save medical expenses. Early decline monitoring or illness prevention is provided to high-risk people.\u003c/p\u003e \u003cp\u003eClinicians more precisely evaluate mortality risk by using the Clavien-Dindo classification and CCI. Patient-centered care is improved by many technologies. People who have a high frequency of Grade III or IV problems can benefit from intensive care or surgery, claims Clavien-Dindo. Decision-making and patient outcomes are improved by integrating CCI with age, comorbidities, and surgery type.\u003c/p\u003e \u003cp\u003eThe results highlight the need of looking at the effects of several variables. In addition to concerns, the CCI evaluate a patient's risk burden. They are able to predict patient mortality and recovery more accurately. Therefore, in complicated instances with several dynamic components, such those involving the elderly or those with chronic diseases, the CCI is helpful.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cb\u003eSummary of Key Findings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Comprehensive Complication Index (CCI) and Clavien-Dindo classification accurately predict death for orthopedic surgery patients hospitalized to the critical care unit. While Clavien-Dindo accurately assigns a severity rating, the Comprehensive Complication Index (CCI) provides a more thorough risk assessment by taking cumulative complications into consideration. According to the study, mortality rose with high Clavien-Dindo and CCI scores. Think about surgical results in all their complexity.\u003c/p\u003e \u003cp\u003eThe ROC curve indicates that a CCI score of 28.60 has high sensitivity and specificity in predicting mortality. The evidence suggests that CCI is useful in surgical disciplines other than orthopedic surgery.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNumerous study methodologies enhance the prediction of therapeutic postoperative mortality risk. The report provide important details. When doing non-orthopedic surgery, CCI has to be evaluated. Mortality in general, neurological, and cardiovascular surgery is predicted by the CCI. Because postoperative problems differ from patient to patient, it is necessary to investigate the CCI's ability to predict mortality in several regions. The long-term predictive ability of the Clavien-Dindo and CCI scores is shown by extensive longitudinal research.\u003c/p\u003e \u003cp\u003eTo more accurately forecast postoperative mortality, future research should integrate the Charlson Comorbidity Index (CCI) with other clinical tools and attributes such socioeconomic status, patient functional ability, and post-discharge care. A risk prediction model that takes into consideration intricate medical, social, and environmental relationships is created with further study. Predictions are improved by including objects in the model. According to the study, orthopedic surgery mortality is predicted by the Charlson Comorbidity Index (CCI). This is especially post-operative. To enhance patient outcomes, this approach need to be evaluated in other surgical specialties and incorporated into clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the\u003c/p\u003e\n\u003cp\u003eHatay Mustafa Kemal University Clinical Research Ethics Committee\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used anonymized retrospective data extracted from the Intensive Care Unit records of Hatay Mustafa Kemal University. Because no direct patient contact occurred and no identifiable information was used, the Ethics Committee approved a waiver of informed consent for all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the information declared in all authors\u0026rsquo; certification and conflict-of-interest forms,\u003c/p\u003e\n\u003cp\u003eno financial support, grants, or external funding were received for this study.\u003c/p\u003e\n\u003cp\u003eNo author received payments, honoraria, consulting fees, stock ownership, or royalties related to the content of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number:\u003c/em\u003e\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe data supporting the findings of this study were obtained from the ICU database of Hatay Mustafa Kemal University and include demographic, clinical, laboratory, surgical, and postoperative complication data.\u003c/li\u003e\n \u003cli\u003eBecause the dataset contains sensitive medical information and is subject to institutional confidentiality rules, raw data cannot be shared publicly.\u003c/li\u003e\n \u003cli\u003eDe-identified data may be provided by the corresponding author upon reasonable request, subject to institutional approval and ethical regulations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors completed conflict-of-interest disclosure forms.\u003c/p\u003e\n\u003cp\u003eAccording to the forms (Menekşe Okşar, Nedim Kılı\u0026ccedil;kıran, Serhat Hakkoymaz, Filiz Şaşmaz, Selim Turhanoğlu, and Fatih G\u0026ouml;kalp),\u003c/p\u003e\n\u003cp\u003eno authors report any financial, personal, or professional conflicts of interest that could influence the research or its interpretation.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMenekşe Okşar (Hatay Mustafa Kemal University, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudy design, clinical data extraction, drafting of the manuscript, manuscript editing, integrity and accuracy of data.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eNedim Kılı\u0026ccedil;kıran (Hatay Mustafa Kemal University, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStatistical analysis, data interpretation, manuscript revision.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePınar Tuncay (Adıyaman Training and Research Hospital, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLiterature review, manuscript editing, verification of clinical classifications.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSerhat Hakkoymaz (Birecik State Hospital, Şanlıurfa, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData interpretation, manuscript revision.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFiliz Şaşmaz (Ankara Etlik City Hospital, Ankara, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eManuscript revision, clinical input.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSelim Turhanoğlu (Hatay Mustafa Kemal University, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eManuscript review, clinical consultation.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFatih G\u0026ouml;kalp (Hatay Mustafa Kemal University, Turkey)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eManuscript review, additional clinical interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eMade substantial contributions to the conception, design, data collection, analysis, or interpretation.\u003c/li\u003e\n \u003cli\u003eDrafted and/or critically revised the manuscript.\u003c/li\u003e\n \u003cli\u003eApproved the final version.\u003c/li\u003e\n \u003cli\u003eAgree to be accountable for all aspects of the work and to address any questions regarding accuracy or integrity.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003cbr\u003eThe authors declare that they have \u003cstrong\u003eno known financial, personal, or professional conflicts of interest\u003c/strong\u003e that could have influenced the work reported in this manuscript. No external funding, sponsorship, or commercial relationships were involved in the design, data collection, analysis, or publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003eThe data supporting the findings of this study were obtained from the\u0026nbsp;\u003cstrong\u003eIntensive Care Unit database of Hatay Mustafa Kemal University\u003c/strong\u003e and include patient clinical, demographic, surgical, and postoperative complication records.\u003cbr\u003eDue to\u0026nbsp;\u003cstrong\u003eethical restrictions and patient confidentiality requirements\u003c/strong\u003e, the raw datasets cannot be made publicly available.\u003cbr\u003eDe-identified data may be made available \u003cstrong\u003efrom the corresponding author upon reasonable request\u003c/strong\u003e, subject to institutional approval and compliance with data protection regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSlankamenac K, Graf R, Barkun J, Puhan MA, Clavien PA. The comprehensive complication index: a novel continuous scale to measure surgical morbidity. Ann Surg. 2013;258(1):1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SLA.0b013e318296c732\u003c/span\u003e\u003cspan address=\"10.1097/SLA.0b013e318296c732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 23728278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockenhuber N, Schweighofer F, Seibert FJ. Diagnosis, therapy and prognosis of Pipkin fractures. Chirurg. 1994;65(11):976\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026uuml;edi TP, Murphy WM, Ağuş H. \u0026ccedil;ev. \u003cem\u003eKırık tedavisinde AO kuralları.\u003c/em\u003e İstanbul: Nobel Tıp Kitabevleri; 2006. s.441\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickstein DL, Kabaso D, Rocher AB, Luebke JI, Wearne SL, Hof PR. Changes in the structural complexity of the aged brain. Aging Cell. 2007;6(3):275\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedi A, Le TT. Subtrochanteric femur fractures. Orthop Clin North Am. 2004;35(4):473\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly-Pettersson P, Samuelsson B, Muren O, Unbeck M, Gordon M, Stark A, et al. Waiting time to surgery is correlated with an increased risk of serious adverse events during hospital stay in patients with hip fracture: a cohort study. Int J Nurs Stud. 2017;69:91\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuman E, Ateş Y. Femur cisim kırıkları. TOTBİD Dergisi. 2008;7(1\u0026ndash;2):45\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeeney JA, Ingari JV, Mentzer KD, Powell ET. Closed intramedullary nailing of femoral shaft fractures in an echelon III facility. Mil Med. 2009;174(2):124\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBone LB, Johnson KD, Weigelt J, Scheinberg R. Early and delayed stabilization of femur fractures: a prospective randomized study. J Bone Joint Surg Am. 1989;71(3):336\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarula N, Dannenberg AJ, Olin JW, Bhatt DL, Johnson KW, Nadkarni G, et al. Pathology of peripheral artery disease in patients with critical limb ischemia. J Am Coll Cardiol. 2018;72(18):2152\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong CC, Tan JH, Lim SH, Nather A. Multiple limb salvage attempts for diabetic foot infections: is it worth it? Bone Joint J. 2017;99\u0026ndash;B(11):1502\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTisi PV, Than MM. Type of incision for below knee amputation. Cochrane Database Syst Rev 2014;(4):CD003749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirth GB, Masquijo JJ, Kontio K, editors. Transtibial Ertl amputation for children and adolescents: a case series and literature review. \u003cem\u003eJ Child Orthop.\u003c/em\u003e 2011;5(5):357\u0026ndash;62. Miller RD. \u003cem\u003eMiller Anestezi.\u003c/em\u003e 6. baskı. İzmir: G\u0026uuml;ven Kitapevi; 2010. pp. 2409\u0026ndash;34. Mariano ER. Ortopedik cerrahide anestezi. In: Butterworth JF, Wasnick JD, Mackey DC, editors. \u003cem\u003eMorgan \u0026amp; Mikhail Klinik Anesteziyoloji.\u003c/em\u003e İstanbul: G\u0026uuml;neş Tıp Kitapevleri; 2015. pp. 789\u0026ndash;804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozek-Langenecker SA, Ahmed AB, Afshari A, Albaladejo P, Aldecoa C, Barauskas G, et al. Management of severe perioperative bleeding: guidelines from the European Society of Anaesthesiology. Eur J Anaesthesiol. 2017;34(6):332\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ccedil;etiner M. Cerrahi girişim sonrası kanamalarda ayırıcı tanı. In: 39. Ulusal Hematoloji Kongresi Kitabı; 2003:30\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFanelli G, Casati A, Aldegheri G, et al. Cardiovascular effects of two different regional anaesthetic techniques for unilateral leg surgery. Acta Anaesthesiol Scand. 1998;42(1):80\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasati A, Cappelleri G, Aldegheri G, et al. Total intravenous anesthesia, spinal anesthesia or combined sciatic-femoral nerve block for outpatient knee arthroscopy. Minerva Anestesiol. 2004;70(10):493\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeah VW, Singh G, Yang KY, Yeo SJ, Lo NN, Seow KH. Thirty-day mortality and morbidity after total knee arthroplasty. Ann Acad Med Singap. 2007;36:1010\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazıcı M, Kayrak M, Ko\u0026ccedil; F. Cerrahi \u0026ouml;ncesi kardiyak değerlendirme. Genel Tıp Derg. 2008;18:129\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeiner S, Westlake B, Dutton RP. Patterns of surgical care and complications in elderly adults. J Am Geriatr Soc. 2014;62(5):829\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuach LH, Jayamaha S, Whitehouse SL, Crawford R, Pulle CR, Bell JJ. Comparison of the Charlson comorbidity index with the ASA score for predicting 12-month mortality in acute hip fracture. Injury. 2020;51(4):1004\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClavien PA, Barkun J, de Oliveira ML, Vauthey JN, Dindo D, Schulick RD, et al. The Clavien\u0026ndash;Dindo classification of surgical complications. Ann Surg. 2009;250(2):187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu LN, Yang B, Li GP, Gao DW. Assessment of complications after liver surgery: Two novel grading systems applied to patients undergoing hepatectomy. J Huazhong Univ Sci Technolog Med Sci. 2017;37(3):352\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Orthopedic Surgery, Mortality, Scoring Systems, CCI, Upgrade","lastPublishedDoi":"10.21203/rs.3.rs-8221964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8221964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eICU mortality after orthopaedic surgery is predicted by CCI. In this case, we evaluated the accuracy of the CCI's mortality prediction. The research investigated if CCI, a composite assessment of circumstances, outperform existing methods in predicting death rates.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom January 1, 2020, to August 1, 2024, patients undergoing orthopedic surgery in intensive care units were the focus of this study. The trial was open to anyone aged 18 and older who underwent orthopedic surgery and were admitted to the critical care unit. Participants in the study did not have cancer. Ages, sexes, BMIs, co-morbidities, length of surgery, intensive care unit stay, and need for blood transfusions were recorded for each patient. While Clavien-Dindo evaluated severity, the Charlson Comorbidity Index (CCI) calculated a cumulative risk score for each patient depending on their problems.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMortality, Clavien-Dindo, and CCI scores were all connected in the research. Mortality rose with higher Clavien-Dindo and CCI scores. According to the Receiver Operating Characteristic (ROC) study, a mortality risk prediction with a 28.60 CCI score has 100% sensitivity and 99.59% specificity. This research found that CCI predicts patient mortality rates.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAccording to the research, orthopedic surgery mortality is predicted by Clavien-Dindo and CCI ratings. The Commodity Channel Index (CCI), a forecasting tool, helps identify high-risk patients and improve post-operative treatment.\u003c/p\u003e","manuscriptTitle":"The Role of the Comprehensive Complication Index in Predicting Mortality in Patients Admitted to the Intensive Care Unit After Orthopedic Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:48:44","doi":"10.21203/rs.3.rs-8221964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20d4017e-2a5d-4920-9d75-91b6278635df","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T08:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:48:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8221964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8221964","identity":"rs-8221964","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}